软件开发的复杂性问题讨论

软件开发是个复杂的系统 ,它符合复杂系统的所有基本特性。文中首先详细研究了软件开发的层次结构 ,然后分析了软件开发的过程 ,最后提出了用Agent技术对软件开发过程进行仿真的思路     

软件开发的复杂问题讨论

软件开发是个复杂的系统，它符合复杂系统的所有基本特性。文中首先详细研究了软件开发的层次结构，然后分析了软件开发的过程，最后提出了用Ａgent技术对软件开发过程进行仿真的思路。     

The Use of Software Complexity Metrics in Software Maintenance

This paper reports on a modest study which relates seven different software complexity metrics to the experience of maintenance activities performed on a medium size software system. Three different versions of the system that evolved over a period of three years were analyzed in this study. A major revision of the system, while still in its design phase, was also analyzed.     

线性时间选择算法时间复杂度深入研究

算法研究是计算机科学的核心领域之一。文中针对元素选择问题及解此问题的线性时间选择算法进行了深入研究,详细分析并论证了期望情况下与最坏情况下线性时间选择算法的时间复杂度,并对拟中位数元素选择问题进行了深层次的拓展,通过计算比较求出了线性时间下的最小复杂度因子。以期有助于该算法在相关领域的应用。     

时间复杂度的几种计算方法

算法的时间复杂度是反映算法优劣的重要指标,是《数据结构》的重要理论基础,是学习和教学过程中贯穿始终的主要线索。但是由于概念的抽象和计算方法的繁琐,使算法时间复杂度成为最难理解和掌握的问题之一。在总结教学经验的基础上,该文提出几种常用的时间复杂度计算方法,使对该知识点的教学和学习变得系统和简单。     

演化算法的时间复杂性

演化算法在工程领域取得了广泛的应用,但是其基础理论尚未完全建立。文章讨论了演化算法的时间复杂性,提出一个估计(1+1)EA平均计算时间的简单方法,对几个实例的应用显示了该方法分析演化算法计算时间的有效性。     

k分搜索的时间复杂度分析

分治策略的思想是将一个规模较大的问题分解为多个形式相同的子问题来解决。搜索是指在一个排好序的数组中寻找与给定数值x相等的元素,传统的搜索算法是遍历,而二分搜索是一种基于分治策略的搜索算法。二分搜索是将数组每次分为相等的两部分,将待查元素x与数组中间的元素比较,若相等则搜索成功;否则将搜索范围缩小为原来的一半,之后以此类推,直到找到待查元素,与遍历相比,二分搜索复杂度明显降低。以二分搜索为基础,每次可以将数组分为更多部分,即k分搜索,探寻k为何值时k分搜索算法的时间复杂度最低,能够对搜索算法进一步优化。通过分析、归纳与证明,得出k分搜索的时间复杂度为O(klog_kn),由于该函数是递增的,因此二分搜索是效率最高的搜索算法,复杂度为O(log₂n);此外,当k=n时,k分搜索退化为遍历,复杂度退化为O(n)。     

PHP项目中利用数组降低时间复杂度

在PHP项目“高职院校共享型专业教学资源库平台”的开发中．通过研究与实践提出如何利用数组来降低因多重循环而引起的时间复杂度的问题。特别是当程序需要多次与数据库进行交互时,用此种方法来优化程序代码,将会使程序的运行速度大大加快,同时能降低系统消耗,具有很好的效果。     

Defining Asymptotic Parallel Time Complexity of Data-dependent Algorithms

The scientific research community has reached a stage of maturity where its strong need for high-performance computing has diffused into also everyday life of engineering and industry algorithms. In efforts to satisfy this need, parallel computers provide an efficient and economical way to solve large-scale and/or time-constrained problems. As a consequence, the end-users of these systems have a vested interest in defining the asymptotic time complexity of parallel algorithms to predict their performance on a particular parallel computer. The asymptotic parallel time complexity of data-dependent algorithms depends on the number of processors, data size, and other parameters. Discovering the main other parameters is a challenging problem and the clue in obtaining a good estimate of performance order. Great examples of these types of applications are sorting algorithms, searching algorithms and solvers of the traveling salesman problem (TSP). This article encompasses all the knowledge discovery aspects to the problem of defining the asymptotic parallel time complexity of data-dependent algorithms. The knowledge discovery methodology begins by designing a considerable number of experiments and measuring their execution times. Then, an interactive and iterative process explores data in search of patterns and/or relationships detecting some parameters that affect performance. Knowing the key parameters which characterise time complexity, it becomes possible to hypothesise to restart the process and to produce a subsequent improved time complexity model. Finally, the methodology predicts the performance order for new data sets on a particular parallel computer by replacing a numerical identification. As a case of study, a global pruning traveling salesman problem implementation (GP-TSP) has been chosen to analyze the influence of indeterminism in performance prediction of data-dependent parallel algorithms, and also to show the usefulness of the defined knowledge discovery methodology. The subsequent hypotheses generated to define the asymptotic parallel time complexity of the TSP were corroborated one by one. The experimental results confirm the expected capability of the proposed methodology; the predictions of performance time order were rather good comparing with real execution time (in the order of 85%).     

浅析算法设计与算法时间复杂度

算法设计与算法时间复杂度分析是数据结构中研究算法的重要内容。本文主要介绍如何针对实际问题设计效率较高的算法以及对算法的时间复杂度进行分析的方法。     

基于证据推理的软件项目复杂性评估

软件项目复杂性是软件项目管理中很重要的指标。该文定义了软件项目复杂性的概念,建立了软件项目复杂性模型,提出了基于证据推理的软件项目复杂性评估模型和方法。并结合一个简单的例子,验证了方法的可行性。     

基于McCabe的软件复杂性度量与控制策略

软件复杂性度量与控制是软件开发面临的主要问题。本文通过对软件复杂性的定量分析,提出了软件复杂性的控制策略,该策略对开发高质量、高可靠性与高可维护性软件有一定的指导作用。